Two-step quantile regression estimation of panel data model with measurement error
In this paper,the problem of parameter estimation for panel data models containing measurement errors is considered systematically.The corrected values of the independent variables of the fixed-effects panel model are obtained by eliminating the effect of measurement error in the independent variables through the factor score strategy.Subsequently,the first-order difference quantile regression method and fixed-effect quantile regression method are employed to obtain the estimators,respectively.Meanwhile,the estimated confidence intervals are obtained by the Bootstrap algorithm.The Monte-Carlo simulation results indicate that the newly proposed two-step quantile regression estimation methods outperform the traditional quantile regression method in terms of relative deviation and mean square error under the two fixed-effects panel models of homoskedasticity and heteroskedasticity.Furthermore,the empirical analysis based on the panel data of actual cigarette sales in each state of the U.S.demonstrates that the proposed method has advantages in eliminating measurement errors and improving the accuracy of estimates,and could provide reliable basis for solving practical and complex dynamic problems.
万方数据
维普
